An efficient fake account identification in social media networks: Facebook and Instagram using NSGA-II algorithm

The widespread use of online social networks (OSNs) has made them prime targets for cyber attackers, who exploit these platforms for various malicious activities. As a result, a whole industry of black-market services has emerged, selling services based on the sale of fake accounts. Because of the massive rise of OSNs, the number of fraudulent accounts rapidly expands. Hence, this research focuses on detecting fraudulent profiles on Instagram and Facebook and aims to find an optimal subset of features that can effectively differentiate between real and fake accounts. The problem has been formulated as a multiobjective optimization task, aiming to maximize the classification accuracy while minimizing the number of selected features. NSGA-II (non-dominated sorting genetic algorithm II) is employed as the optimization algorithm to explore the trade-offs between these conflicting objectives. In the current study, a novel approach for feature selection using the NSGA-II optimization algorithm to detect fake accounts is proposed. The proposed methodology relies on input data comprising features characterizing the profiles under investigation. The selected features are utilized to train a machine learning model. The model’s performance is evaluated using various metrics, including precision, recall, F1-score, and receiver operating characteristic (ROC) curve. The final prediction model achieved accuracy values ranging from 90 to 99.88%. The results indicated that the model, utilizing features selected by the NSGA-II algorithm, delivered high prediction accuracy while using less than 31% of the total feature space. This efficient feature selection allowed for the precise differentiation between fake and real users, demonstrating the model’s effectiveness with a minimal number of input variables. Furthermore, the results of experiments demonstrate that the proposed approach achieves better performance as compared to other existing approaches. This research paper focuses on explainability, which refers to the ability to understand and interpret the decisions and outcomes of machine learning models.